Construction of a correct wireframe model from a feature point cloud is a challenging issue. Feature point clouds are generally derived from two sensing technologies: (i) three dimensional laser radars (3D Ladar), and (ii) stereo parallax measurements from two, or more, different passive observation cameras (Stereo).
Typically, a 3D Ladar produces dense point clouds. However, because the sensing is usually done from only one position, foreground objects most often completely obscure what is behind them. Since the obscuring foreground objects are in close proximity to the background objects, known wire-frame construction algorithms generally bridge between these objects.
Stereo parallax measurements can be made from multiple camera positions. Thus, in principle, stereo can be less subject to problems with foreground obscurations. However, in practice these obstructions may cause known wire-frame construction algorithms to bridge gaps in the point cloud. Thus, adjacent buildings, separated by a relatively small space, are often treated as a single structure by known wire framing algorithms in stereo.
Thus, a need exists in the art for improved structure discovery in a point cloud.